The Potential of Menstrual Blood-Derived Stem Cells in Differentiation to Epidermal Lineage: A Preliminary Report.
Bibliographic record
Abstract
BACKGROUND: Menstrual blood-derived stem cells (MenSCs) are a novel source of stem cells that can be easily isolated non-invasively from female volunteered donor without ethical consideration. These mesenchymal-like stem cells have high rate of proliferation and possess multi lineage differentiation potency. This study was undertaken to isolate the MenSCs and assess their potential in differentiation into epidermal lineage. METHODS: About 5-10 ml of menstrual blood (MB) was collected using sterile Diva cups inserted into vagina during menstruation from volunteered healthy fertile women aged between 22-30 years. MB was transferred into Falcon tubes containing phosphate buffered saline (PBS) without Ca2(+) or Mg2(+) supplemented with 2.5 µg/ml fungizone, 100 µg/mL streptomycin, 100 U/mL penicillin and 0.5 mM EDTA. Mononuclear cells were separated using Ficoll-Hypaque density gradient centrifugation and washed out in PBS. The cell pellet was suspended in DMEM-F12 medium supplemented with 10% FBS and cultured in tissue culture plates. The isolated cells were co-cultured with keratinocytes derived from the foreskin of healthy newborn male aged 2-10 months who was a candidate for circumcision for differentiation into epidermal lineage. RESULTS: The isolated MenSCs were adhered to the plate and exhibited spindle-shaped morphology. Flow cytometric analysis revealed the expression of mesenchymal markers of CD10, CD29, CD73, and CD105 and lack of hematopoietic stem cells markers. An early success in derivation of epidermal lineage from MenSCs was visible. CONCLUSION: The MenSCs are a real source to design differentiation to epidermal cells that can be used non-invasively in various dermatological lesions and diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".